Overview

Dataset statistics

Number of variables14
Number of observations534425
Missing cells37977
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.1 MiB
Average record size in memory112.0 B

Variable types

Numeric10
Categorical4

Warnings

Exit_Reason has constant value "AgentAnswered" Constant
Call_Start has a high cardinality: 521961 distinct values High cardinality
Party_Name has a high cardinality: 147 distinct values High cardinality
QueuedTime is highly correlated with WaitTime and 1 other fieldsHigh correlation
RingTime is highly correlated with WaitTime vs QueuedTimeHigh correlation
WaitTime is highly correlated with QueuedTime and 1 other fieldsHigh correlation
Queue + Ring is highly correlated with QueuedTime and 1 other fieldsHigh correlation
WaitTime vs QueuedTime is highly correlated with RingTimeHigh correlation
Exit_Reason is highly correlated with channelHigh correlation
channel is highly correlated with Exit_ReasonHigh correlation
AgentTime has 36472 (6.8%) missing values Missing
Call_Start is uniformly distributed Uniform
df_index has unique values Unique
QueuedTime has 32124 (6.0%) zeros Zeros
TalkTime has 7451 (1.4%) zeros Zeros
HoldTime has 439575 (82.3%) zeros Zeros
WrapTime has 24445 (4.6%) zeros Zeros

Reproduction

Analysis started2021-02-27 23:03:23.394319
Analysis finished2021-02-27 23:04:11.820385
Duration48.43 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct534425
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314456.1161
Minimum13
Maximum613887
Zeros0
Zeros (%)0.0%
Memory size4.1 MiB
2021-02-28T00:04:12.084185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile30486.2
Q1166542
median319137
Q3467008
95-th percentile584959.8
Maximum613887
Range613874
Interquartile range (IQR)300466

Descriptive statistics

Standard deviation177027.7056
Coefficient of variation (CV)0.5629647399
Kurtosis-1.168492094
Mean314456.1161
Median Absolute Deviation (MAD)150031
Skewness-0.0733312154
Sum1.680532098 × 1011
Variance3.133880855 × 1010
MonotocityStrictly increasing
2021-02-28T00:04:12.280893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40981
 
< 0.1%
3204571
 
< 0.1%
3020221
 
< 0.1%
2958771
 
< 0.1%
2979241
 
< 0.1%
3081631
 
< 0.1%
3102101
 
< 0.1%
3040651
 
< 0.1%
3061121
 
< 0.1%
4801911
 
< 0.1%
Other values (534415)534415
> 99.9%
ValueCountFrequency (%)
131
< 0.1%
141
< 0.1%
161
< 0.1%
181
< 0.1%
191
< 0.1%
ValueCountFrequency (%)
6138871
< 0.1%
6138861
< 0.1%
6138841
< 0.1%
6138831
< 0.1%
6138821
< 0.1%

Call_Start
Categorical

HIGH CARDINALITY
UNIFORM

Distinct521961
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2020-08-03 11:15:51.000002
 
5
2020-09-15 09:18:49.999999
 
4
2020-07-07 11:11:22.999998
 
4
2020-08-14 13:48:41.999999
 
4
2020-10-07 11:17:35.000002
 
4
Other values (521956)
534404 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters13895050
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509708 ?
Unique (%)95.4%

Sample

1st row2020-01-02 08:00:21.000001
2nd row2020-01-02 08:00:56.000001
3rd row2020-01-02 08:02:52.000002
4th row2020-01-02 08:03:16.000004
5th row2020-01-02 08:03:31.000003
ValueCountFrequency (%)
2020-08-03 11:15:51.0000025
 
< 0.1%
2020-09-15 09:18:49.9999994
 
< 0.1%
2020-07-07 11:11:22.9999984
 
< 0.1%
2020-08-14 13:48:41.9999994
 
< 0.1%
2020-10-07 11:17:35.0000024
 
< 0.1%
2020-09-09 15:20:19.0000003
 
< 0.1%
2020-06-04 13:20:24.9999993
 
< 0.1%
2020-06-08 18:35:56.0000003
 
< 0.1%
2020-03-02 15:30:53.0000043
 
< 0.1%
2020-06-30 12:15:03.0000013
 
< 0.1%
Other values (521951)534389
> 99.9%
2021-02-28T00:04:14.207306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-06-013086
 
0.3%
2020-08-033043
 
0.3%
2020-06-153022
 
0.3%
2020-09-083001
 
0.3%
2020-06-222996
 
0.3%
2020-06-292984
 
0.3%
2020-08-242971
 
0.3%
2020-08-172961
 
0.3%
2020-09-142960
 
0.3%
2020-07-272960
 
0.3%
Other values (51474)1038866
97.2%

Most occurring characters

ValueCountFrequency (%)
03777023
27.2%
21765585
12.7%
91422997
 
10.2%
11326957
 
9.5%
-1068850
 
7.7%
:1068850
 
7.7%
534425
 
3.8%
.534425
 
3.8%
3518955
 
3.7%
4485556
 
3.5%
Other values (4)1391427
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10688500
76.9%
Other Punctuation1603275
 
11.5%
Dash Punctuation1068850
 
7.7%
Space Separator534425
 
3.8%

Most frequent character per category

ValueCountFrequency (%)
03777023
35.3%
21765585
16.5%
91422997
 
13.3%
11326957
 
12.4%
3518955
 
4.9%
4485556
 
4.5%
5444158
 
4.2%
8329692
 
3.1%
7317477
 
3.0%
6300100
 
2.8%
ValueCountFrequency (%)
:1068850
66.7%
.534425
33.3%
ValueCountFrequency (%)
-1068850
100.0%
ValueCountFrequency (%)
534425
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13895050
100.0%

Most frequent character per script

ValueCountFrequency (%)
03777023
27.2%
21765585
12.7%
91422997
 
10.2%
11326957
 
9.5%
-1068850
 
7.7%
:1068850
 
7.7%
534425
 
3.8%
.534425
 
3.8%
3518955
 
3.7%
4485556
 
3.5%
Other values (4)1391427
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13895050
100.0%

Most frequent character per block

ValueCountFrequency (%)
03777023
27.2%
21765585
12.7%
91422997
 
10.2%
11326957
 
9.5%
-1068850
 
7.7%
:1068850
 
7.7%
534425
 
3.8%
.534425
 
3.8%
3518955
 
3.7%
4485556
 
3.5%
Other values (4)1391427
 
10.0%

Exit_Reason
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
AgentAnswered
534425 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters6947525
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgentAnswered
2nd rowAgentAnswered
3rd rowAgentAnswered
4th rowAgentAnswered
5th rowAgentAnswered
ValueCountFrequency (%)
AgentAnswered534425
100.0%
2021-02-28T00:04:14.561334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T00:04:14.728350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
agentanswered534425
100.0%

Most occurring characters

ValueCountFrequency (%)
e1603275
23.1%
A1068850
15.4%
n1068850
15.4%
g534425
 
7.7%
t534425
 
7.7%
s534425
 
7.7%
w534425
 
7.7%
r534425
 
7.7%
d534425
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5878675
84.6%
Uppercase Letter1068850
 
15.4%

Most frequent character per category

ValueCountFrequency (%)
e1603275
27.3%
n1068850
18.2%
g534425
 
9.1%
t534425
 
9.1%
s534425
 
9.1%
w534425
 
9.1%
r534425
 
9.1%
d534425
 
9.1%
ValueCountFrequency (%)
A1068850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6947525
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1603275
23.1%
A1068850
15.4%
n1068850
15.4%
g534425
 
7.7%
t534425
 
7.7%
s534425
 
7.7%
w534425
 
7.7%
r534425
 
7.7%
d534425
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6947525
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1603275
23.1%
A1068850
15.4%
n1068850
15.4%
g534425
 
7.7%
t534425
 
7.7%
s534425
 
7.7%
w534425
 
7.7%
r534425
 
7.7%
d534425
 
7.7%

Party_Name
Categorical

HIGH CARDINALITY

Distinct147
Distinct (%)< 0.1%
Missing16
Missing (%)< 0.1%
Memory size4.1 MiB
Alex Dillon
 
16188
Daniel Schirmer
 
13479
Dave Lee Wincek
 
12990
Timmy Moran
 
12555
John Gene Vura
 
12522
Other values (142)
466675 

Length

Max length17
Median length16
Mean length15.37343495
Min length8

Characters and Unicode

Total characters8215702
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKyle Younglas
2nd rowLuis Torres
3rd rowDaniel Schirmer
4th rowMike Bilfield
5th rowChris Smucny
ValueCountFrequency (%)
Alex Dillon 16188
 
3.0%
Daniel Schirmer 13479
 
2.5%
Dave Lee Wincek12990
 
2.4%
Timmy Moran 12555
 
2.3%
John Gene Vura12522
 
2.3%
Alex Baltas 12410
 
2.3%
Butch Herten 11747
 
2.2%
Mike Pascaru 11567
 
2.2%
Alex Hord 10944
 
2.0%
Dustin Pollock 10937
 
2.0%
Other values (137)409070
76.5%
2021-02-28T00:04:15.282792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alex56548
 
5.2%
mike43662
 
4.0%
john25028
 
2.3%
dan21513
 
2.0%
matt21444
 
2.0%
dillon19628
 
1.8%
joe18294
 
1.7%
connor17387
 
1.6%
daniel15936
 
1.5%
schirmer15935
 
1.5%
Other values (146)838955
76.7%

Most occurring characters

ValueCountFrequency (%)
2111031
25.7%
e687609
 
8.4%
a551805
 
6.7%
n492022
 
6.0%
r425517
 
5.2%
l384336
 
4.7%
o353388
 
4.3%
i351440
 
4.3%
t244085
 
3.0%
h182184
 
2.2%
Other values (41)2432285
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4990384
60.7%
Space Separator2111031
25.7%
Uppercase Letter1113889
 
13.6%
Other Punctuation398
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e687609
13.8%
a551805
11.1%
n492022
9.9%
r425517
 
8.5%
l384336
 
7.7%
o353388
 
7.1%
i351440
 
7.0%
t244085
 
4.9%
h182184
 
3.7%
c180670
 
3.6%
Other values (15)1137328
22.8%
ValueCountFrequency (%)
M151290
13.6%
S100705
9.0%
D100223
9.0%
B95895
 
8.6%
A91506
 
8.2%
C88815
 
8.0%
J76192
 
6.8%
T62611
 
5.6%
K61756
 
5.5%
L39118
 
3.5%
Other values (14)245778
22.1%
ValueCountFrequency (%)
2111031
100.0%
ValueCountFrequency (%)
*398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6104273
74.3%
Common2111429
 
25.7%

Most frequent character per script

ValueCountFrequency (%)
e687609
 
11.3%
a551805
 
9.0%
n492022
 
8.1%
r425517
 
7.0%
l384336
 
6.3%
o353388
 
5.8%
i351440
 
5.8%
t244085
 
4.0%
h182184
 
3.0%
c180670
 
3.0%
Other values (39)2251217
36.9%
ValueCountFrequency (%)
2111031
> 99.9%
*398
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8215702
100.0%

Most frequent character per block

ValueCountFrequency (%)
2111031
25.7%
e687609
 
8.4%
a551805
 
6.7%
n492022
 
6.0%
r425517
 
5.2%
l384336
 
4.7%
o353388
 
4.3%
i351440
 
4.3%
t244085
 
3.0%
h182184
 
2.2%
Other values (41)2432285
29.6%

channel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
SEO
390552 
PPC
143873 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1603275
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEO
2nd rowSEO
3rd rowSEO
4th rowSEO
5th rowSEO
ValueCountFrequency (%)
SEO390552
73.1%
PPC143873
 
26.9%
2021-02-28T00:04:15.632337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-28T00:04:15.767899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
seo390552
73.1%
ppc143873
 
26.9%

Most occurring characters

ValueCountFrequency (%)
S390552
24.4%
E390552
24.4%
O390552
24.4%
P287746
17.9%
C143873
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1603275
100.0%

Most frequent character per category

ValueCountFrequency (%)
S390552
24.4%
E390552
24.4%
O390552
24.4%
P287746
17.9%
C143873
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1603275
100.0%

Most frequent character per script

ValueCountFrequency (%)
S390552
24.4%
E390552
24.4%
O390552
24.4%
P287746
17.9%
C143873
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1603275
100.0%

Most frequent character per block

ValueCountFrequency (%)
S390552
24.4%
E390552
24.4%
O390552
24.4%
P287746
17.9%
C143873
 
9.0%

QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct150
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.40043224
Minimum0
Maximum149
Zeros32124
Zeros (%)6.0%
Memory size4.1 MiB
2021-02-28T00:04:15.933688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q311
95-th percentile61
Maximum149
Range149
Interquartile range (IQR)8

Descriptive statistics

Standard deviation21.76125573
Coefficient of variation (CV)1.754878806
Kurtosis11.2277466
Mean12.40043224
Median Absolute Deviation (MAD)2
Skewness3.220036816
Sum6627101
Variance473.5522508
MonotocityNot monotonic
2021-02-28T00:04:16.139416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4119064
22.3%
371771
13.4%
254098
 
10.1%
535068
 
6.6%
133639
 
6.3%
032124
 
6.0%
611947
 
2.2%
710704
 
2.0%
910685
 
2.0%
810254
 
1.9%
Other values (140)145071
27.1%
ValueCountFrequency (%)
032124
 
6.0%
133639
 
6.3%
254098
10.1%
371771
13.4%
4119064
22.3%
ValueCountFrequency (%)
1496
 
< 0.1%
14819
 
< 0.1%
14741
< 0.1%
14666
< 0.1%
14599
< 0.1%

RingTime
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.091103522
Minimum0
Maximum29
Zeros1699
Zeros (%)0.3%
Memory size4.1 MiB
2021-02-28T00:04:16.325189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q311
95-th percentile16
Maximum29
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.257729251
Coefficient of variation (CV)0.5262235539
Kurtosis0.4774156763
Mean8.091103522
Median Absolute Deviation (MAD)3
Skewness0.8115948365
Sum4324088
Variance18.12825837
MonotocityNot monotonic
2021-02-28T00:04:16.518357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
656145
10.5%
553854
10.1%
751033
9.5%
849051
9.2%
448666
9.1%
943184
 
8.1%
1036164
 
6.8%
333311
 
6.2%
1129370
 
5.5%
1223453
 
4.4%
Other values (20)110194
20.6%
ValueCountFrequency (%)
01699
 
0.3%
17082
 
1.3%
220237
3.8%
333311
6.2%
448666
9.1%
ValueCountFrequency (%)
292
 
< 0.1%
281
 
< 0.1%
273
 
< 0.1%
2692
< 0.1%
25140
< 0.1%

TalkTime
Real number (ℝ≥0)

ZEROS

Distinct927
Distinct (%)0.2%
Missing1489
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean182.7073251
Minimum0
Maximum926
Zeros7451
Zeros (%)1.4%
Memory size4.1 MiB
2021-02-28T00:04:16.717727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q149
median135
Q3249
95-th percentile563
Maximum926
Range926
Interquartile range (IQR)200

Descriptive statistics

Standard deviation174.9070802
Coefficient of variation (CV)0.9573074319
Kurtosis2.362593766
Mean182.7073251
Median Absolute Deviation (MAD)94
Skewness1.550173873
Sum97371311
Variance30592.48669
MonotocityNot monotonic
2021-02-28T00:04:16.908631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07451
 
1.4%
204123
 
0.8%
193996
 
0.7%
183994
 
0.7%
213977
 
0.7%
223857
 
0.7%
173838
 
0.7%
233726
 
0.7%
243689
 
0.7%
163668
 
0.7%
Other values (917)490617
91.8%
ValueCountFrequency (%)
07451
1.4%
11760
 
0.3%
21959
 
0.4%
31807
 
0.3%
41524
 
0.3%
ValueCountFrequency (%)
92623
< 0.1%
92539
< 0.1%
92436
< 0.1%
92315
 
< 0.1%
92231
< 0.1%

HoldTime
Real number (ℝ≥0)

ZEROS

Distinct163
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.545215886
Minimum0
Maximum162
Zeros439575
Zeros (%)82.3%
Memory size4.1 MiB
2021-02-28T00:04:17.108966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile29
Maximum162
Range162
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.76823334
Coefficient of variation (CV)4.129228139
Kurtosis28.8240296
Mean4.545215886
Median Absolute Deviation (MAD)0
Skewness5.199652205
Sum2429077
Variance352.2465825
MonotocityNot monotonic
2021-02-28T00:04:17.308857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0439575
82.3%
121134
 
4.0%
210348
 
1.9%
38096
 
1.5%
45520
 
1.0%
54146
 
0.8%
63416
 
0.6%
72629
 
0.5%
82029
 
0.4%
91382
 
0.3%
Other values (153)36150
 
6.8%
ValueCountFrequency (%)
0439575
82.3%
121134
 
4.0%
210348
 
1.9%
38096
 
1.5%
45520
 
1.0%
ValueCountFrequency (%)
16264
< 0.1%
16170
< 0.1%
16074
< 0.1%
15977
< 0.1%
15879
< 0.1%

WrapTime
Real number (ℝ≥0)

ZEROS

Distinct1035
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.5061926
Minimum0
Maximum1034
Zeros24445
Zeros (%)4.6%
Memory size4.1 MiB
2021-02-28T00:04:17.505766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median77
Q3185
95-th percentile463
Maximum1034
Range1034
Interquartile range (IQR)171

Descriptive statistics

Standard deviation163.1594447
Coefficient of variation (CV)1.240697806
Kurtosis5.94289689
Mean131.5061926
Median Absolute Deviation (MAD)69
Skewness2.201907425
Sum70280197
Variance26621.00438
MonotocityNot monotonic
2021-02-28T00:04:17.705877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024445
 
4.6%
414514
 
2.7%
513530
 
2.5%
312987
 
2.4%
611584
 
2.2%
79686
 
1.8%
88553
 
1.6%
97310
 
1.4%
106568
 
1.2%
115819
 
1.1%
Other values (1025)419429
78.5%
ValueCountFrequency (%)
024445
4.6%
11943
 
0.4%
25494
 
1.0%
312987
2.4%
414514
2.7%
ValueCountFrequency (%)
103411
< 0.1%
103310
< 0.1%
103214
< 0.1%
103111
< 0.1%
103013
< 0.1%

WaitTime
Real number (ℝ≥0)

HIGH CORRELATION

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.49153576
Minimum0
Maximum150
Zeros1277
Zeros (%)0.2%
Memory size4.1 MiB
2021-02-28T00:04:17.901511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median13
Q321
95-th percentile66
Maximum150
Range150
Interquartile range (IQR)11

Descriptive statistics

Standard deviation21.29351322
Coefficient of variation (CV)1.039137011
Kurtosis10.73462864
Mean20.49153576
Median Absolute Deviation (MAD)4
Skewness3.080602565
Sum10951189
Variance453.4137052
MonotocityNot monotonic
2021-02-28T00:04:18.273078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1042129
 
7.9%
939632
 
7.4%
1237045
 
6.9%
1136215
 
6.8%
835294
 
6.6%
1430443
 
5.7%
1328111
 
5.3%
725654
 
4.8%
1622494
 
4.2%
1520153
 
3.8%
Other values (141)217255
40.7%
ValueCountFrequency (%)
01277
 
0.2%
1116
 
< 0.1%
2266
 
< 0.1%
3523
 
0.1%
44070
0.8%
ValueCountFrequency (%)
150124
< 0.1%
149133
< 0.1%
148138
< 0.1%
147140
< 0.1%
146123
< 0.1%

Queue + Ring
Real number (ℝ≥0)

HIGH CORRELATION

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.49153576
Minimum0
Maximum150
Zeros1277
Zeros (%)0.2%
Memory size4.1 MiB
2021-02-28T00:04:18.468398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median13
Q321
95-th percentile66
Maximum150
Range150
Interquartile range (IQR)11

Descriptive statistics

Standard deviation21.29351322
Coefficient of variation (CV)1.039137011
Kurtosis10.73462864
Mean20.49153576
Median Absolute Deviation (MAD)4
Skewness3.080602565
Sum10951189
Variance453.4137052
MonotocityNot monotonic
2021-02-28T00:04:18.663207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1042129
 
7.9%
939632
 
7.4%
1237045
 
6.9%
1136215
 
6.8%
835294
 
6.6%
1430443
 
5.7%
1328111
 
5.3%
725654
 
4.8%
1622494
 
4.2%
1520153
 
3.8%
Other values (141)217255
40.7%
ValueCountFrequency (%)
01277
 
0.2%
1116
 
< 0.1%
2266
 
< 0.1%
3523
 
0.1%
44070
0.8%
ValueCountFrequency (%)
150124
< 0.1%
149133
< 0.1%
148138
< 0.1%
147140
< 0.1%
146123
< 0.1%

WaitTime vs QueuedTime
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.091103522
Minimum0
Maximum29
Zeros1699
Zeros (%)0.3%
Memory size4.1 MiB
2021-02-28T00:04:18.847933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q311
95-th percentile16
Maximum29
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.257729251
Coefficient of variation (CV)0.5262235539
Kurtosis0.4774156763
Mean8.091103522
Median Absolute Deviation (MAD)3
Skewness0.8115948365
Sum4324088
Variance18.12825837
MonotocityNot monotonic
2021-02-28T00:04:19.029268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
656145
10.5%
553854
10.1%
751033
9.5%
849051
9.2%
448666
9.1%
943184
 
8.1%
1036164
 
6.8%
333311
 
6.2%
1129370
 
5.5%
1223453
 
4.4%
Other values (20)110194
20.6%
ValueCountFrequency (%)
01699
 
0.3%
17082
 
1.3%
220237
3.8%
333311
6.2%
448666
9.1%
ValueCountFrequency (%)
292
 
< 0.1%
281
 
< 0.1%
273
 
< 0.1%
2692
< 0.1%
25140
< 0.1%

AgentTime
Real number (ℝ≥0)

MISSING

Distinct1782
Distinct (%)0.4%
Missing36472
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean321.1318056
Minimum1
Maximum2037
Zeros0
Zeros (%)0.0%
Memory size4.1 MiB
2021-02-28T00:04:19.210218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1121
median253
Q3443
95-th percentile873
Maximum2037
Range2036
Interquartile range (IQR)322

Descriptive statistics

Standard deviation267.1778363
Coefficient of variation (CV)0.8319880859
Kurtosis2.033491387
Mean321.1318056
Median Absolute Deviation (MAD)152
Skewness1.366200854
Sum159908546
Variance71383.9962
MonotocityNot monotonic
2021-02-28T00:04:19.409868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291587
 
0.3%
281571
 
0.3%
271560
 
0.3%
331537
 
0.3%
301532
 
0.3%
341527
 
0.3%
251525
 
0.3%
321522
 
0.3%
311512
 
0.3%
261500
 
0.3%
Other values (1772)482580
90.3%
(Missing)36472
 
6.8%
ValueCountFrequency (%)
120
 
< 0.1%
293
 
< 0.1%
3256
< 0.1%
4303
0.1%
5370
0.1%
ValueCountFrequency (%)
20371
< 0.1%
20311
< 0.1%
19541
< 0.1%
19381
< 0.1%
19341
< 0.1%

Interactions

2021-02-28T00:03:45.849486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:46.124229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:46.448392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:46.687464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:46.949559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:47.206552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:47.458532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:47.708448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:47.955623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:48.193685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:48.442488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:48.685564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:48.936382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:49.198857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:49.442644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:49.683647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:49.941359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:50.184028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:50.433164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:50.676061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:50.926797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:51.170565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:51.436681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:51.692219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:51.940565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:52.183227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:52.420802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:52.845317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:53.135654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:53.424331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:53.786929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:54.059092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:54.310348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:54.580475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:54.827186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:55.064534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:55.311775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:55.557126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:55.812417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:56.061814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:56.331584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:56.596485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:56.853142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:57.117455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:57.364663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:57.626520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:57.881647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:58.130553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:58.367901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:58.601431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:58.845967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:59.095621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:59.344640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:59.596955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:03:59.836209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:00.249699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:00.510571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:00.760150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:01.002977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:01.264690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:01.513857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:01.764540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:02.011028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:02.277572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:02.528358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:02.788938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:03.051046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:03.297216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:03.551059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:03.796432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:04.041994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:04.286134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:04.531493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:04.780916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:05.036376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:05.285214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:05.528214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:05.780281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:06.042228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:06.292532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:06.537789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:06.786964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:07.195442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:07.441284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:07.695168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:07.928473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:08.177602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:08.418669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:08.670328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-28T00:04:08.920234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-28T00:04:19.587922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-28T00:04:19.792122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-28T00:04:19.988107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-28T00:04:20.193253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-28T00:04:20.375396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-28T00:04:09.620315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-28T00:04:10.279863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-28T00:04:11.036972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-28T00:04:11.354891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTimeAgentTime
0132020-01-02 08:00:21.000001AgentAnsweredKyle YounglasSEO09184.00.0318.0999502.0
1142020-01-02 08:00:56.000001AgentAnsweredLuis TorresSEO05454.00.015.0555469.0
2162020-01-02 08:02:52.000002AgentAnsweredDaniel SchirmerSEO111411.00.095.0121211506.0
3182020-01-02 08:03:16.000004AgentAnsweredMike BilfieldSEO06182.089.08.0666279.0
4192020-01-02 08:03:31.000003AgentAnsweredChris SmucnySEO15428.00.035.0665463.0
5202020-01-02 08:03:46.000002AgentAnsweredIan FlanaganSEO0827.03.015.088845.0
6212020-01-02 08:04:21.000003AgentAnsweredKenneth CombesPPC19522.044.092.010109658.0
7222020-01-02 08:05:00.000004AgentAnsweredMac BlankSEO1356.02.010.044368.0
8232020-01-02 08:05:38.000003AgentAnsweredAlex ShroyerSEO11514.02.022.016161538.0
9242020-01-02 08:07:53.000003AgentAnsweredMichael DiZonnoPPC08100.00.05.0888105.0

Last rows

df_indexCall_StartExit_ReasonParty_NamechannelQueuedTimeRingTimeTalkTimeHoldTimeWrapTimeWaitTimeQueue + RingWaitTime vs QueuedTimeAgentTime
5344156138772021-02-21 12:05:26.999998AgentAnsweredJohnathan JordanSEO1212132.00.00.0242412132.0
5344166138782021-02-21 12:07:37.000004AgentAnsweredMatthew DuffSEO1111210.00.00.0222211210.0
5344176138792021-02-21 12:08:27.000004AgentAnsweredJunior FetchetSEO88133.00.02.016168135.0
5344186138802021-02-21 12:13:19.000001AgentAnsweredMatt TashjianSEO151568.00.042.0303015110.0
5344196138812021-02-21 12:13:54.000002AgentAnsweredMatthew DuffSEO18692.00.02.02424694.0
5344206138822021-02-21 12:14:41.999997AgentAnsweredLauren BaschSEO17540.00.00.02222540.0
5344216138832021-02-21 12:17:38.999996AgentAnsweredMatt TashjianSEO1414121.00.02.0282814123.0
5344226138842021-02-21 12:18:55.000003AgentAnsweredMeredith ChesneySEO44289.00.01.0884290.0
5344236138862021-02-21 12:22:32.999998AgentAnsweredMatt TashjianSEO171755.00.012.034341767.0
5344246138872021-02-21 12:24:08.999997AgentAnsweredJunior FetchetSEO77201.00.042.014147243.0